grad_outputs->push_back(Transpose(scope, grad_inputs[0], inverted_perm));
  grad_outputs->push_back(NoGradient());
  return scope.status();
}
REGISTER_GRADIENT_OP("Transpose", TransposeGrad);

Status ReverseSequenceGrad(const Scope& scope, const Operation& op,
                           const std::vector<Output>& grad_inputs,
                           std::vector<Output>* grad_outputs) {
  auto seq_lengths = op.input(1);

  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
  TensorShape y_shape({2, 5});
  auto y = Transpose(scope_, x, {1, 0});
  RunTest(x, x_shape, y, y_shape);
}

TEST_F(ArrayGradTest, ReverseSequenceGrad) {
  TensorShape shape({5, 2, 5});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto seq_lengths = Const(scope_, {1, 2, 3, 4, 5});
  // batch_dim defaults to 0.